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User mobility and Quality-of-Experience aware placement of Virtual Network Functions in 5G

机译:用户移动性和体验质量意识到虚拟网络功能的放置在5G中

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摘要

Virtual Network Functions (VNFs) in cloud servers of Fifth Generation (5G) network systems are responsible for executing offloaded codes from mobile users. Placement of VNFs in the cloud is very complicated to get on-time execution service due to many reasons including users' mobility and resource heterogeneity, which often cause VNF relocations from one data center to another. Minimizing service delay (i.e., maximizing user Quality-of-Experience) for the user applications and the number of VNF relocations are the two main design goals of VNF placement problem; however, they do oppose each other. In this paper, we have formulated the above problem as a Multi-objective Integer Linear Programming (MILP), which is proven to be an NP-hard one. The proposed optimization framework trades-off between the number of VNF relocations and user Quality-of-Experience. We then develop an Artificial Intelligence based meta-heuristic Ant Colony Optimization (ACO) algorithm to achieve sub-optimal placement of VNFs within polynomial time. The performance analysis results, carried out in Cloudsim, depict that the proposed system outperforms the state-of-the-art works significantly in terms of user satisfaction and VNF relocation overhead.
机译:第五代(5G)网络系统的云服务器中的虚拟网络功能(VNFS)负责从移动用户执行卸载代码。由于包括用户的移动性和资源异质性的许多原因,云中的VNFS的放置非常复杂,以获得可持续时间执行服务,这通常会导致VNF从一个数据中心重新定位到另一个数据中心。最小化用户应用程序的服务延迟(即,最大化用户质量的质量)和VNF重定位的数量是VNF放置问题的两个主要设计目标;但是,他们互相反对。在本文中,我们将上述问题制定为多目标整数线性编程(MILP),这被证明是一个NP-Hard一个。建议的优化框架在VNF重新定位和用户质量质量之间进行交易。然后,我们开发了一种人工智能的基于元启发式蚁群优化(ACO)算法,以在多项式时间内实现VNFS的次优次放置。在Cloudsim中进行的性能分析结果描绘了所提出的系统在用户满意度和VNF重定位开销方面显着优于最先进的工作。

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